Skill UI

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

This technical skill is designed to verify the compatibility of the Codex CLI with skills compiled from MDA source. Loading this skill confirms that the CLI loader successfully parses the SKILL.md format, adhering to the agentskills.io v1 envelope subset. It is primarily used for testing build pipelines, metadata integrity, and ensuring robust compatibility between MDA compilation and runtime ingestion.

This skill serves as a compatibility test to ensure that skills compiled from MDA (Metadata Definition Architecture) sources are correctly recognized and installed by the OpenClaw platform. It validates the integrity of the skill loading process, confirming successful parsing and integration.

SocialClaw is a powerful, agent-first skill designed for comprehensive social media automation. It allows users to schedule and publish content across 13 major platforms (including X, LinkedIn, Instagram, TikTok, and more) using a single workspace API key. Streamline your marketing campaigns by managing content creation, media uploads, scheduling validation, and performance analytics from one central workflow.

This skill runs the Bumblebee scanner on macOS/Linux to perform read-only inventory and exposure checks. It detects compromised packages, developer tools, and host configurations against known security advisories. It is primarily used for supply-chain incident response and vulnerability assessment, providing developers with a clear picture of local system exposure.

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

This tool accesses a persistent FTS5 knowledge base containing previously indexed project documentation, local content, and session memory. Instead of feeding entire raw sources into the conversation context, it allows users to perform targeted, efficient searches using specific queries and source scoping. This is ideal for quickly retrieving technical details, project specifications, or historical context without lengthy re-reading.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

This orchestrator converts complex markdown files (specs, reports, code reviews, slide decks) into single-file, lightly interactive HTML. It intelligently classifies the input content and routes it to the appropriate sub-skill (document, review, or slides), ensuring that long-form technical artifacts maintain density, hierarchy, and shareable structure in a browser-native format.

A specialized skill for achieving asymptotic performance gains when classical algorithms are already near optimal. It leverages advanced mathematical techniques and probabilistic data structures (e.g., Bloom Filters, HyperLogLog, FFT, MinHash/LSH) to handle massive datasets (n ≥ 10⁶) by accepting bounded approximations or exploiting deep structural properties. Requires strict adherence to error parameter and complexity justification protocols.

Nextflow is a powerful workflow language and runtime for building reproducible, portable, and scalable data pipelines. It is dominant in bioinformatics, enabling complex dataflow programming and managing workflows across diverse environments, including local machines, HPC clusters (SLURM), and cloud platforms (AWS, Azure). This skill covers the full lifecycle: running established pipelines (like nf-core), developing custom modular workflows, configuring containerization, and ensuring computational reproducibility.